Give chance a chance: Modeling density to enhance scatter plot quality through random data sampling

Enrico Bertini, Giuseppe Santucci

    Research output: Contribution to journalArticle

    Abstract

    The problem of visualizing huge amounts of data is well known in information visualization. Dealing with a large number of items forces almost any kind of Infovis technique to reveal its limits in terms of expressivity and scalability. In this paper we focus on 2D scatter plots, proposing a 'feature preservation' approach, based on the idea of modeling the visualization in a virtual space in order to analyze its features (e.g., absolute density, relative density, etc.). In this way we provide a formal framework to measure the visual overlapping, obtaining precise quality metrics about the visualization degradation and devising automatic sampling strategies able to improve the overall image quality. Metrics and algorithms have been improved through suitable user studies.

    Original languageEnglish (US)
    Pages (from-to)95-110
    Number of pages16
    JournalInformation Visualization
    Volume5
    Issue number2
    DOIs
    StatePublished - Jun 2006

    Fingerprint

    Visualization
    Sampling
    Image quality
    Scalability
    Degradation

    Keywords

    • Numerosity
    • Overplotting
    • Quality metrics
    • Sampling

    ASJC Scopus subject areas

    • Computer Vision and Pattern Recognition

    Cite this

    Give chance a chance : Modeling density to enhance scatter plot quality through random data sampling. / Bertini, Enrico; Santucci, Giuseppe.

    In: Information Visualization, Vol. 5, No. 2, 06.2006, p. 95-110.

    Research output: Contribution to journalArticle

    @article{69896e78296049d4a9ab2e8628156efb,
    title = "Give chance a chance: Modeling density to enhance scatter plot quality through random data sampling",
    abstract = "The problem of visualizing huge amounts of data is well known in information visualization. Dealing with a large number of items forces almost any kind of Infovis technique to reveal its limits in terms of expressivity and scalability. In this paper we focus on 2D scatter plots, proposing a 'feature preservation' approach, based on the idea of modeling the visualization in a virtual space in order to analyze its features (e.g., absolute density, relative density, etc.). In this way we provide a formal framework to measure the visual overlapping, obtaining precise quality metrics about the visualization degradation and devising automatic sampling strategies able to improve the overall image quality. Metrics and algorithms have been improved through suitable user studies.",
    keywords = "Numerosity, Overplotting, Quality metrics, Sampling",
    author = "Enrico Bertini and Giuseppe Santucci",
    year = "2006",
    month = "6",
    doi = "10.1057/palgrave.ivs.9500122",
    language = "English (US)",
    volume = "5",
    pages = "95--110",
    journal = "Information Visualization",
    issn = "1473-8716",
    publisher = "Palgrave Macmillan Ltd.",
    number = "2",

    }

    TY - JOUR

    T1 - Give chance a chance

    T2 - Modeling density to enhance scatter plot quality through random data sampling

    AU - Bertini, Enrico

    AU - Santucci, Giuseppe

    PY - 2006/6

    Y1 - 2006/6

    N2 - The problem of visualizing huge amounts of data is well known in information visualization. Dealing with a large number of items forces almost any kind of Infovis technique to reveal its limits in terms of expressivity and scalability. In this paper we focus on 2D scatter plots, proposing a 'feature preservation' approach, based on the idea of modeling the visualization in a virtual space in order to analyze its features (e.g., absolute density, relative density, etc.). In this way we provide a formal framework to measure the visual overlapping, obtaining precise quality metrics about the visualization degradation and devising automatic sampling strategies able to improve the overall image quality. Metrics and algorithms have been improved through suitable user studies.

    AB - The problem of visualizing huge amounts of data is well known in information visualization. Dealing with a large number of items forces almost any kind of Infovis technique to reveal its limits in terms of expressivity and scalability. In this paper we focus on 2D scatter plots, proposing a 'feature preservation' approach, based on the idea of modeling the visualization in a virtual space in order to analyze its features (e.g., absolute density, relative density, etc.). In this way we provide a formal framework to measure the visual overlapping, obtaining precise quality metrics about the visualization degradation and devising automatic sampling strategies able to improve the overall image quality. Metrics and algorithms have been improved through suitable user studies.

    KW - Numerosity

    KW - Overplotting

    KW - Quality metrics

    KW - Sampling

    UR - http://www.scopus.com/inward/record.url?scp=33745469319&partnerID=8YFLogxK

    UR - http://www.scopus.com/inward/citedby.url?scp=33745469319&partnerID=8YFLogxK

    U2 - 10.1057/palgrave.ivs.9500122

    DO - 10.1057/palgrave.ivs.9500122

    M3 - Article

    AN - SCOPUS:33745469319

    VL - 5

    SP - 95

    EP - 110

    JO - Information Visualization

    JF - Information Visualization

    SN - 1473-8716

    IS - 2

    ER -